INTEGRATION OF DIGITAL MANAGEMENT AND RESOURCE EFFICIENCY OF ENTERPRISES IN THE CONTEXT OF SUSTAINABLE DEVELOPMENT

Abstract

In this study, we examine the integration of digital management and resource efficiency within the framework of sustainable development and the concept of natural resource efficiency (NRE). We analyze how enterprises can move from fragmented management practices toward integrated, data-driven systems that simultaneously increase productivity and reduce environmental pressure. We argue that digital transformation should not be treated as a purely technological shift but as a structural reconfiguration of managerial models that embeds sustainability into operational decision-making. We systematize the core characteristics of digital management, including process digitalization, the creation of new value propositions, and the development of digital partnerships. We evaluate modern approaches to measuring resource efficiency, such as Data Envelopment Analysis (DEA), benchmarking, Life Cycle Assessment (LCA), and Carbon Footprint Analysis. We demonstrate how enterprises can combine these methods with digital twins and artificial intelligence tools to construct a real-time monitoring and adaptive management system. We show that digital twins enable continuous data collection and process modeling, DEA and benchmarking provide quantitative efficiency diagnostics, and AI algorithms generate dynamic optimization scenarios. Unlike existing studies that consider digital transformation and resource efficiency separately, this article proposes for the first time an integrated three-level model that combines digital twins (for monitoring and modeling processes), DEA and benchmarking (for quantitative performance assessment), and artificial intelligence (for dynamic adjustment of management decisions). We find that this integrated architecture strengthens operational resilience, reduces waste, optimizes energy consumption, and improves ESG indicators. We illustrate the practical applicability of the model through the case of a large telecommunications enterprise operating under conditions of infrastructural risk and rapid digital expansion. We also identify the rebound effect as a critical risk, arguing that enterprises must monitor behavioral responses alongside technical indicators to avoid offsetting environmental gains. We conclude that enterprises need to develop rational models for integrating digital management platforms with AI-based resource efficiency assessment tools in order to minimize waste, reduce costs, and strengthen the synergy between digital transformation and sustainable development.